X-ray imaging is a common procedure, in medical imaging the energy range for the x-rays is typically 10 keV to 200 keV, in non-destructive testing or security screening the energy may be higher. In this range the x-rays reacts with matter mainly through Compton effect and Photoelectric effect. In the first case only a part of the energy of the x-ray photon is passed on to the electron and the x-ray continues with decreased energy after this scattering event. In the latter case all the energy is passed to the electron and the x-ray is completely absorbed.
A challenge for x-ray imaging detectors is to extract maximum information from the detected x-rays to provide input to an image of an object where the object is depicted in terms of density, composition and structure. It is still common to use film-screen as detector but mostly the detectors today provide a digital image.
The detector needs to convert the incident x-rays into electrons, this typically take place through Photo-effect or through Compton interaction and the resulting electron are usually creating secondary visible light until its energy is lost and this light is in turn detected by a photo-sensitive material. There are also detectors, less common, which are based on semiconductors such as amorphous Selenium or Silicon and in this case the electrons created by the x-ray is creating electric charge in terms of electrons and hole-pairs which are collected through an applied electric field with enough strength.
In computed tomography (CT) it is preferable to have a large detector; the detector size in the direction of the rotation determines the field-of-view and a large z-coverage tangential to the direction of the rotation (length in the scan direction) is essential for both fast full body acquisition in spiral mode scanning and the capability of covering entire organs in a single rotation. Imaging a large object (like a human patient) with a large detector however results in large scatter-to-primary ratios which degrades image quality by reducing contrast and introducing artifacts. Several different methods exist for combating the deteriorating effects of scatter. In essence, all fall in one of the following two categories:                A. Eliminating object scatter from reaching the detector        B. Estimating the scatter distribution and correcting for it either by means of simple subtraction of the spatial scatter profile or some type of de-convolution.        
Methods for eliminating scatter to reach the detector include air gaps and anti-scatter grids and the use of the latter techniques come at the cost of absorbing primary x-rays as well thus reducing the dose efficiency of the system.
Methods for estimating the spatial distribution of the object scatter include:                A. Beam stop methods (U.S. Pat. No. 6,618,466 B1) where small and highly absorbing objects (such as lead beads) are placed in the beam path between the x-ray source and the objects. The corresponding locations on the detector are thus blind to primary radiation and the detector elements only measure the scattered radiation at that point. Since the scatter profile is spatially slowly varying (low frequency components) the beam stops can be rather sparsely distributed and still yield an adequate measure of the scatter profile.        B. Monte Carlo, or semi-analytical simulation models, whereby either the outline of the object, or a reconstructed slice, is used to estimate the object scatter profile on the detector. The second method is a key step in current iterative reconstruction methods.        
The main purpose of scatter rejection and compensation methods to date has been to remove image artifacts and increase image contrast. With the advent of spectral photon counting computed tomography, where each photon is counted individually and assigned to an energy bin dependent on the amount of energy it deposits in the detector, accurate knowledge of the amount of scatter in each projection has become even more important. The reason is that one of the promises of spectral photon counting CT is to be able to perform material basis decomposition in the projection domain and thus achieve quantitative CT. Material basis decomposition is performed by solving a maximum likelihood problem. The counts in the bins are used to obtain a set of line integrals of the material basis coefficients in each projection and if the counts include an unknown number of events from scattered radiation, the line integral estimates will be biased which, in the reconstructed image, translates to biased material basis coefficients. The maximum likelihood method for obtaining the line integral estimates from the bin counts is well described by Roessl and Proksa in “K-edge imaging in x-ray computed tomography using multi-bin photon counting detectors”, Physics in Medicine and Biology, vol 52, pp. 4679-96, 2007.
In conclusion, accurate scatter estimates are necessary to unlock the benefits of spectral photon-counting computed tomography.
U.S. Pat. No. 8,183,535 B2 describes a photon-counting energy sensitive detector intended for use mainly in computed tomography. A main feature of is the use of at least two levels of silicon diodes mounted in an edge-on geometry. This is illustrated in FIGS. 10a, 10b, 10c, 10d and 11 of U.S. Pat. No. 8,183,535 B2. A second feature is to use thin tungsten (or any other suitable x-ray absorber) lamella on the backside of each diode. The tungsten lamella serves as a built-in anti-scatter grid, reducing both object scatter and the secondary events which are generated from Compton interaction in the detector material due to the low atomic number of silicon.
It is of general interest to obtain improved image quality by providing efficient scatter estimation and/or correction for this and other types of photon-counting x-ray detectors.